Since a video signal obtained from a solid-state image sensing element such as a CCD or the like contains noise components, an edge emphasis process also emphasizes such noise components, resulting in an image with a poor S/N.
Conventionally, in order to avoid such problem, a base clip process is executed in the edge emphasis process. For example, the base clip process is executed as follows. After a middle/high-frequency signal is obtained by applying a BPF (band-pass filter) to an input signal, a given value is subtracted from the absolute value signal of the middle/high-frequency signal. In this case, levels that assume negative values are determined as noise levels, and are clipped to zero, thus removing noise. Furthermore, the signal that has undergone the above process is multiplied by a given coefficient to attain edge emphasis, i.e., to emphasize a high-frequency signal, and a signal obtained by restoring the original sign of the emphasized signal is added to the input signal. In this manner, an edge-emphasized signal from which noise components have been removed can be obtained.
When a luminance signal undergoes nonlinear conversion such as γ correction or the like, a high-frequency signal is relatively emphasized on the dark side with respect to the bright side. To avoid this, a method of changing an edge correction amount depending on a luminance signal is known.
However, since the amount of noise contained in a video signal obtained from a solid-state image sensing element changes depending on a signal level input to the solid-state image sensing element, if a uniform base clip process is done, an appropriate base clip process for a given input level may turn into an inappropriate one for another input level.
Even when the edge correction amount is changed depending on the luminance signal, an effect of making noise inconspicuous is obtained by dropping the signal level, but an effect of removing noise cannot be expected.
The relationship before and after the aforementioned processes of a high-frequency signal will be explained below with reference to FIG. 20. In FIG. 20, a indicates a relationship without any processes. By calculating the absolute value of the BPF result, input/output relationship b is obtained. By clipping levels, which assume negative values after subtraction of a given value, to zero, relationship c is obtained. Relationship d is obtained by restoring the original sign, and relationship e is obtained by finally applying a gain.
This base clip process is uniformly done for an image, as described above.
However, since a person experiences different noise levels depending on whether an object on which noise is superposed has less or large frequency change, it is preferable to change the base clip process depending on an object. That is, as the frequency characteristics of an object have a stronger resemblance to those of noise, a person is more likely to not perceive noise as noise but to perceive it as a signal that the object naturally has. Hence, the base clip process is not so required. By contrast, as the frequency characteristics of an object are farther from those of noise, noise becomes more conspicuous, and the necessity of the base clip process increases.
In the conventional method, since the base clip process is set independently of the frequency characteristics of an object, a given base clip amount is applied even to a portion that does not require high base clip effect, thus bringing about an image blur. By contrast, a sufficiently large base clip amount cannot be applied to a portion that requires high base clip effect, thus producing noise.